Portfolio Project Description Checker

How Portfolio Project Description Checker works

Press Load model once: your browser downloads Gemma 3 1B instruction-tuned (web build) (728 MB) from this site and keeps it in its cache. Then add your text and press Check locally. The model runs on this device using your graphics card, so nothing you enter is sent to a server. Until the model is loaded, or if your device cannot run it, the tool falls back to a simple keyword check and labels the result as the fallback.

Gemma 3 1B instruction-tuned (web build) is a small open language model from Google. The tool sends it the instruction below plus your text, asks for a JSON answer with fixed fields, and shows each field as a card. It runs at temperature 0, so the same input gives the same answer on the same device, though different graphics cards can word it slightly differently.

Inputs longer than 6,000 characters are cut to that length before they reach the model, and the result says so. The model only sees what you paste: it does not look anything up.

The instruction also carries one worked example: the built-in sample with the answer the larger Gemma 4 E2B model gave for it. That example is what lets this smaller model follow the answer format reliably.

The instruction the model gets

You review one portfolio project description the way a hiring manager would. Find the problem the project solves, the author's own role, the tools and technologies, and the outcome (users, numbers, results, what was learned). Quote or closely restate the description. If something is missing, say so; do not invent users, metrics or features.

What it returns

• Problem it solves: The problem or goal, as stated, or 'Not stated'.
• Your role: What the author personally did; note if it is unclear whether it was solo or team work.
• Tools and tech: Languages, frameworks, services and methods mentioned.
• Outcome: Results, numbers, users or lessons as stated, or 'Not stated'.
• What a reviewer would ask: Two to four questions the description leaves open.

Worked example

The model's output for the built-in sample, recorded on a desktop browser. Your run may word things slightly differently.

Recipe App. I built a recipe app using React and Firebase. Users can search recipes and save favorites. It was a fun project and I learned a lot about state management.

Problem it solves

Users can search recipes and save favorites

Your role

I built a recipe app

Tools and tech

React Firebase

Outcome

Users can search recipes and save favorites. I learned a lot about state management.

What a reviewer would ask

What specific state management techniques did you learn? What was the scope or complexity of the recipe database? Were there any specific challenges encountered during development?

The model

The files are served from this site, versioned so a cached copy is never stale. Nothing is fetched from a third party while you use the tool.

You can switch models in the tool. Also available: Gemma 4 E2B instruction-tuned (web build) (2.0 GB). The larger language model gives more thorough answers but is a bigger download and needs a stronger device.

Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.

Limitations

• Small language models make mistakes: they can miss an item, merge two, or state something more confidently than the text supports. Check the result against your input before you rely on it.
• It needs WebGPU and about 4 GB of memory, so it is offered on desktops and laptops, not on phones and tablets. Devices that cannot run it get the keyword fallback, clearly labelled.
• Results describe the input you gave, not the real-world situation behind it. Review them yourself before acting.

Good uses

• Triage job search and career notes before deeper review
• Prepare consistent labels for repeat workflows
• Reduce manual sorting on small batches
• Keep sensitive drafts in the browser preview